code: def predict_proba(self, X):
      if self.estimator is None:
          raise NotImplementedError()
      return self.estimator.predict_proba(X)
  
file_path: auto-sklearn/examples/80_extending/example_restrict_number_of_hyperparameters.py
imports: from typing import Optional
  from ConfigSpace.configuration_space import ConfigurationSpace
  from ConfigSpace.hyperparameters import UniformIntegerHyperparameter, UniformFloatHyperparameter
  from sklearn.datasets import load_breast_cancer
  from sklearn.model_selection import train_test_split
  from autosklearn.askl_typing import FEAT_TYPE_TYPE
  import autosklearn.classification
  import autosklearn.pipeline.components.classification
  from autosklearn.pipeline.components.classification import AutoSklearnClassificationAlgorithm
  from autosklearn.pipeline.constants import DENSE, UNSIGNED_DATA, PREDICTIONS, SPARSE
label_desc: Use image node_id 4 for calling the CustomRandomForest obj's underlying member method code with example usage: obj.predict_proba(X) and returns: self